AI & Mining: Strong Prospects For Present And Future Growth

Artificial Intelligence has significant potential for use in a number of different aspects of mining operations, including exploration, transportation and metal processing.

Large diversified miners as well as smaller companies are already making use of artificial intelligence in their operations, mainly through driverless trucks and drills as well as interconnected 'smart mining' operations.

Partnerships between mining companies and specialised technology firms will drive the incorporation of artificial intelligence in mining operations over the coming years, while in-house expertise grows.

With the advent of the fourth industrial revolution, artificial intelligence (AI) will, and is already playing, a prominent role in driving miners' transition towards automation and the use of modern technology. From the exploration stage to processing and monitoring, AI has the potential to streamline operations, reduce costs and improve safety concerns within the mining industry. Using vast amounts of data inputs, such as drilling reports and geological surveys, AI - and machine learning - can make predictions and provide recommendations on exploration, resulting in a more efficient process with higher-yield results. AI-powered predictive models will also allow mining companies to improve their metals processing methods, through more accurate and less environmentally damaging techniques. AI will also increasingly be incorporated into operations for the creation of 'smart mines' that will streamline entire operations. Unlike other technological applications such as Blockchain (See 'Blockchain In Mining: Well Poised For Use In Improving ESG Standards', August 1 2018) which is only being tested by miners in pilot projects, AI is already being used by key players in vital areas of their operations. Big diversified miners including BHP Billiton, Rio Tinto and Vale will lead the way in the adoption of AI as they will be financially capable of affording new expensive technology, although smaller miners Goldcorp, Boliden and Fortescue Mining, are also making use of artificial intelligence in a number of capacities. Moving forward, mining companies will continue to partner with specialised technology companies in order to effectively incorporate artificial intelligence into their operations, while also developing their own in-house expertise.

AI May Reduce Transport & Labour Costs

Select Companies - Operational Costs (USDmn)

Source: Company Statements, Fitch Solutions

Below we list the key areas in which AI is currently being implemented by miners along with their future growth opportunities and provide specific user case studies:

Transportation & Safety - The use of AI-powered driverless trucks and drills is the most common way in which miners are currently making use of AI in their operations, and where we see most potential for adoption of the technology across the wider industry moving forward. At a time when mining companies are focused on improving productivity and keeping cash costs capped, automated trucks offer miners significant benefits in both these regards (See 'Global Miners: After A Stellar Year, Future Strength To Depend On Restraint', March 7 2018). For instance, automated trucks make use of machine learning algorithims that use million of images taken from cameras and distance sensors in order to learn the most cost-efficient routes and reduce human error when picking up or offloading ore. Similarly, automated drills working under the direction of predictive models, will optimise productivity through more efficient digging, loading and hauling and increasing machine up-time. As a result, miners can potentially make significant savings in labour and transportation costs that account for a substantial share of their total cash costs (see chart above). Furthermore, the removal of drivers and workers from mining vehicles and equipment will be particularly helpful in reducing safety concerns for miners by eliminating the possibility of accidents taking place. Mining companies already using AI-driven trucks or drills include:

BHP Billiton - Uses an expert AI system to schedule and dispatch trains carrying iron ore from Australian mines to Port Hedland, which has reduced cancellations due to congestion and increased the number of trains run. As the AI system collects more data, the performance of the rail system will continue to improve. Has also partnered with Atlas Copco to trial autonomous drills at the Yandi mine for over two years, with a reported 20% improvement in the optimisation of drills.

Rio Tinto - Through a partnership with Komatsu and Ansaldo STS Rio is the world's largest owner and operator of autonomous haulage system (AHS) trucks in its operations in the Pilbara, Australia. Since launching its 'Mine of the Future' programme in 2008, Rio Tinto now has more than 80 autonomous trucks in operation, with plans to increase this to more than 130 by the end of 2019. The company also makes use of automated drills at the West Angelas mine.

Vale - In 2014 the Brazilian miner signed a USD103mn contract with Swedish digital technology company ABB to automate the transportation of ore from mine site to processing plant at the S11D iron ore project.

Boliden - In 2016 the company teamed up with Volvo to successfully test driverless trucks in the underground operations at Kristineberg mine.

Fortescue Metals - In 2017 the Australian company partnered with Caterpillar's Minestar system to expand its autonomous truck fleet at its Pilbara operations in the Chichester hub and the Solomon hub.

Exploration - We believe the use of AI will prove particularly useful in improving the identification of exploration targets for mining companies over the coming years. As ore grades and resources become increasingly depleted, geologists will find it more difficult to identify new deposits with viable economic reserves for successful project development through traditional methods. By making use of big data, machine learning software is capable of identifying the combination of characteristics that predict the presence of ore in any given area. This will allow miners to not only improve the effectiveness of the exploration process but also potentially save significant costs in the process. So far we have seen how in 2016 top gold miner Goldcorp partnered with IBM's Watson computer system to digest all available data and improve the mineral exploration programme at the Red Lake mine. Furthermore other specialised technology companies such as Kore Geosystems and Goldspot Discoveries, founded in 2015 and 2016, respectively, are focusing on improving exploration methods through the use of artificial intelligence and big data.

Ore Processing - Another potential use of AI in mining will be in improving the metals beneficiation process and optimising entire operations.For instance, when separating pure metal content from ores, mining companies may make use of cyanide as a processing method, which can prove expensive, accounting for up to 40% of the process costs, and is also poisonous. The use of artificial intelligence with predictive models on ore grades can allow miners to use only as much cyanide as is needed, therefore reducing costs and avoiding unnecessary environmental damage. Similar predictive models can also help optimize mining companies short-term and long-term metallurgical planning by inferring local rock properties such as hardness, grade, recovery or the costs of mining. For example, US-based start-up company Datacloud has developed a real-time cloud-based service that helps miners better assess the quality of the rocks they are mining.

Operational Streamlining - More generally, we believe AI has the potential to become an integral part of mining companies' shift towards "smart mining". In conjunction with IoT (Internet of Things) and big data, artificial intelligence can be used to improve the operational efficiency of entire mining operations. The digitisation of operations, whereby equipment is interconnected and monitored centrally through the use of sensors, allows artificial intelligence to interpret real-time data and analytics in order to make predictive models regarding safety, maintenance or optimal productivity. A prime example of this is Rio Tinto's Koodaideri project to build the world's first "intelligent mine" where all assets are networked together and capable of making decisions themselves in microseconds (See 'Technology Integration In Mining: A Deep-Dive Into Australian Miners' Strategies', September 9). The mine, planned to deliver the first tonnes of ore in 2021, will include automated trucks, drills and trains but will also connect all components of the dynamic schedule, from customer through to orebody planning. Through real-time data, operators at the mine will be able to quickly test scenarios to optimise production or operations.

Further Cost Reductions From Smart Mining Ahead

Rio Tinto - Cash Unit Costs At Pilbara (USD/tonne)

Source: Company Reports, Fitch Solutions

Lack Of AI Traction And Labour Disruption To Pose Headwinds

Despite the positive growth prospects for the use of AI in mining, a number of challenges relating to labour displacement and technical considerations will present headwinds to the increasing adoption of the technology by miners over the coming years. As a technology, AI remains in its infancy and is therefore prone to producing limited or ineffective results. For the time being therefore, the use of AI in mining will require close human supervision in order to control for unreliable information. Consequently, while AI may go some way in saving miners labour costs currently used for truck driving or drilling personnel, it will also require the hiring of other highly skilled personnel to monitor that it is being implemented effectively, putting into question its potential for monetary savings. Another risk to the adoption of AI will be the potential social backlash from mining labour unions as a result of labour displacement. As an example, compared to five truck drivers, only one higher-skilled employee may now be required in remote operating centres to maintain the operation of five automated trucks.